Analyzing the

Impact of

COVID-19 on

Global

Population

Dynamics

Team Members:

Names

Siddhartha Alwala

Vishnu Priya Palagiri

Shiva Sai Kumar Tummala

Bhargavi Kondaveeti

Introduction

Our project focuses on visualizing the impact of COVID-19 on global population

dynamics, aiming to provide insights into how the pandemic has affected

different regions and demographic groups.

Through dynamic and interactive visualizations, we aim to uncover patterns,

trends, and correlations within the data, facilitating informed decision-making

and effective response strategies.

Above Workflow Explanation

Data Collection:

Getting relevant datasets from Kaggle, a popular platform for data science datasets.

Initial Visualization:

Utilizing D3.js to visualize the uncleaned dataset, providing an initial overview of the data's structure and

potential insights.

Data Cleaning:

Using Python programming language and libraries like Pandas and NumPy to clean the dataset, handling

missing values, outliers, and inconsistencies.

Refined Visualization:

Using Python's data visualization libraries such as Matplotlib or Seaborn to create more refined visualizations

based on the cleaned dataset, focusing on specific variables of interest.

Dashboard Creation:

Utilizing Microsoft Power BI to design interactive dashboards and reports, incorporating the refined

visualizations to provide a comprehensive view of the analyzed data.

Report Generation:

Creating detailed reports summarizing the analysis findings, insights, and conclusions drawn from the

visualizations and data analysis process.

Data Abstraction-Dataset (Type and

Attributes)

COVID-19 Dataset:

Type: csv

Attributes:

Date: Date of the record

Country/Region: Name of the country or

region

Confirmed Cases: Number of confirmed

COVID-19 cases

Deaths: Number of deaths due to COVID-

Recoveries: Number of recovered COVID-

19 cases, etc..

Life expectancy Dataset:

Type: csv

Attributes:

Country/Region: Name of the country or

region

Population Size: Total population of the

country or region

Life Expectancy: Average life expectancy

in years

Development Status: Development status

of the country or region (e.g., developed,

developing)

GDP (Gross Domestic Product): Economic

output of the country or region

And more etc…

Data Abstraction-Detailed Description of

Dataset:

The COVID-19 dataset contains weekly records of COVID-19 cases reported by

country or region. Each record includes information on confirmed cases,

deaths, and recoveries, allowing for the analysis of the pandemic's impact

over time and across different regions.

The global population dataset provides demographic information for each

country or region, including population size, life expectancy, and

development status. This dataset offers insights into the population dynamics

of different regions and their susceptibility to the pandemic.

The economic indicators includes GDP values for each country or region,

enabling the analysis of the economic impact of the pandemic. GDP serves as

a measure of economic output and can help assess the extent of economic

disruption caused by COVID-19.

Data transformation tasks involve:

Cleaning and preprocessing the datasets to ensure consistency and accuracy.

Handling missing values, standardizing column names, and converting data

types as necessary.

Aggregating and merging relevant information from multiple sources for

analysis.

Ensuring data quality through validation checks and addressing any

discrepancies or inconsistencies.

Normalizing or scaling attributes if required for certain analyses.

Performing feature engineering to create additional features if needed for

enhanced analysis.

Task Abstraction

Task:

Target: The primary objective is to analyze the impact of COVID-19 on global population

dynamics, focusing on key metrics such as confirmed cases, deaths, and recoveries.

Actions:

Analyze trends and patterns in COVID-19 cases across different regions and countries.

Explore correlations between COVID-19 metrics and demographic factors such as population

size, life expectancy, and development status.

Investigate the economic implications of the pandemic by examining the relationship

between COVID-19 cases and GDP.

Compare average life expectancy and GDP across WHO regions to identify disparities and

inform targeted interventions.

Above Workflow Explanation

Data Collection:

Gather COVID-19 data from reliable sources like WHO or national health agencies.

Collect demographic data from reputable sources like the World Bank or United Nations.

Data Analysis:

Analyze COVID-19 trends globally and regionally to identify patterns.

Conduct correlation analysis between COVID-19 metrics and demographic factors.

Use statistical methods to explore the relationship between COVID-19 cases and GDP.

Economic Analysis:

Investigate the impact of COVID-19 on GDP by analyzing economic data.

Explore how COVID-19 cases have affected different sectors of the economy.

Consider the long-term economic implications of the pandemic.

Comparison across WHO Regions:

Compare average life expectancy and GDP across different WHO regions.

Identify disparities and variations in population dynamics and economic development.

Use these comparisons to inform targeted interventions and policy decisions.

Reporting and Recommendations:

Interpret the findings from the analysis.

Provide recommendations for policymakers and stakeholders based on the insights gained.

Summarize the key points and implications for future action.

Implementation using Tools:

D3.js:

Description: D3.js (Data-Driven Documents) was utilized for the initial visualization of the uncleaned dataset. It provided a

platform for creating dynamic and interactive charts and graphs directly within web browsers.

Usage: Through D3.js, we generated interactive visualizations such as bar charts to explore the structure and patterns within

the raw dataset.

Python:

Description: Python, along with libraries like Pandas, NumPy, Matplotlib, and Seaborn, played a crucial role in data

preprocessing, analysis, and visualization.

Usage: Pandas and NumPy were employed for data cleaning tasks, including handling missing values, outliers, and

inconsistencies. Matplotlib and Seaborn were used to create refined visualizations based on the cleaned dataset, showcasing

insights through various types of charts and plots.

Microsoft Power BI:

Description: Microsoft Power BI served as a comprehensive platform for designing interactive dashboards and reports,

integrating visualizations to provide a comprehensive view of the analyzed data.

Usage: Power BI enabled us to create interactive visualizations, including bar charts, line graphs, and maps, and seamlessly

integrate them into interactive dashboards. Additionally, Power BI's data modeling capabilities allowed for the creation of

relationships between different datasets, enhancing the depth of analysis in the final reports.

Results for Analysis-

Demonstration

Initial Visualization Using D3.js Pie Chart

of COVID-19 Cases by WHO Region(pre-

cleaning)

Explanation:

The pie chart provides a clear overview of how COVID-19 cases are

distributed among different WHO regions.

We can see at a glance which regions have the highest number of

confirmed cases, enabling policymakers and health organizations to

allocate resources effectively.

Regions with larger segments may require more attention in terms of

healthcare infrastructure and preventive measures.

Story:

Imagine global health officials convening to discuss strategies for combating

the spread of COVID-19. As they gather around a screen displaying the pie

chart, they immediately notice the significant portion representing the

European region, indicating a high number of confirmed cases.

This prompts discussions on implementing stricter containment measures

and increasing healthcare capacity in Europe.

Similarly, the smaller segments representing regions with fewer cases spark

conversations about sharing resources and best practices to prevent further

outbreaks.

Stacked Bar Chart of COVID-19 Cases by

Country-Using D3.js

Explanation:

The stacked bar chart allows for a detailed examination of COVID-19 cases

within individual countries. By breaking down each bar into segments for

confirmed, deaths, and recovered cases, viewers can see not only the

total number of cases but also the outcomes of those cases. This provides

insights into how countries are managing the pandemic, including their

healthcare systems' capacity to treat patients and mitigate fatalities.

Story:

As policymakers analyze the stacked bar chart, they focus on the

disparities in outcomes among the top 10 countries with the highest

confirmed cases. They notice that while some countries have a high

number of confirmed cases, they've also managed to achieve a substantial

number of recoveries, indicating effective healthcare interventions.

Conversely, countries with a high proportion of deaths prompt discussions

on implementing measures to reduce mortality rates, such as improving

access to critical care facilities and vaccine distribution.

After Cleaning Datasets Using Python And

Visualizing Using Matplotlib: Exploring Global

Health Data

Explanation:

Data Cleaning: Removes extra spaces from column names for

consistency.

Summary Statistics: Computes key statistics for numeric

columns.

Visualization: Displays the distribution of life expectancy using

a histogram.

Story:

In a health research project, data cleaning ensures consistency

in column names, aiding smooth analysis.

Summary statistics reveal crucial insights into life expectancy

trends worldwide.

Visualizing life expectancy distributions highlights variations,

guiding targeted interventions for global health improvement.

Understanding Global Population Dynamics:

Distribution Analysis

Explanation:

Data Preparation: Ensures consistency in column names for

clarity.

Statistical Insight: Reveals key population statistics for

analysis.

Visual Representation: Depicts population distribution via

histogram and KDE.

Story:

In a demographic study, data is prepared by standardizing

column names to streamline analysis. Statistical examination

unveils crucial population trends, essential for informed

decision-making. Visualizing population distributions

illuminates demographic disparities, guiding policy

interventions for sustainable development.

Exploring Global Economic Patterns: Distribution of

GDP

Explanation:

Data Preparation: Cleans column names, ensuring

uniformity.

Statistical Analysis: Computes summary statistics for GDP.

Visualization: Displays GDP distribution via a histogram

with KDE.

Story:

In an economic analysis endeavor, data is prepped by

standardizing column names for clarity. Statistical analysis

uncovers insights into global GDP trends, vital for

economic policy formulation. Visualizing GDP distributions

highlights disparities, informing strategies for balanced

economic development.

Analyzing Numeric Relationships: Correlation

Heatmap

Explanation:

Data Filtering: Retains only numeric columns for correlation

analysis.

Exploratory Analysis: Examines correlations between numeric

variables.

Visual Insight: Presents correlations via heatmap with

annotations.

Story:

In a data exploration endeavor, non-numeric columns are

filtered out to focus on quantitative relationships.

Through exploratory analysis, correlations among numeric

variables are investigated, offering insights into

interdependencies.

Visualizing correlations via a heatmap aids in identifying

patterns and guiding further analysis for informed decision-

making.

Analyzing COVID-19 Trends: Summary Statistics and

Time Series Plot

Explanation:

Statistical Overview: Generates summary statistics for COVID-19

data.

Temporal Analysis: Plots trends of confirmed cases, deaths, and

recoveries over time.

Story:

In assessing the COVID-19 pandemic, summary statistics offer

insights into the overall magnitude and variability of cases.

Meanwhile, the time series plot depicts the progression of

confirmed cases, deaths, and recoveries over time, enabling the

observation of trends and fluctuations.

Through these analyses, policymakers and health authorities can

better understand the impact of the pandemic and formulate

effective response strategies.

Visualizing COVID-19 Metrics on a World Map

Explanation:

Data Loading: Imports the world map shapefile and COVID-19 data.

Data Merging: Merges COVID-19 data with world map data based on country names.

Map Visualization: Plots COVID-19 metrics (confirmed cases, deaths, recoveries) on the world

map.

Story:

In mapping the global impact of COVID-19, the world map shapefile is loaded alongside

COVID-19 data.

By merging these datasets, a comprehensive view of the pandemic's spread is achieved,

allowing for spatial analysis.

The resulting visualizations depict the distribution of confirmed cases, deaths, and recoveries

across countries, aiding in understanding regional variations and informing targeted response

efforts.

Exploring the Relationship

Between Life Expectancy and

COVID-19 Cases

Explanation:

Data Merging: Merges datasets based on the 'Country/Region' column.

Scatter Plot: Visualizes the relationship between life expectancy and confirmed

COVID-19 cases.

Story:

By merging datasets on country/region identifiers, a comprehensive dataset is

created for analysis.

Through a scatter plot, the correlation between life expectancy and confirmed

COVID-19 cases is explored, offering insights into potential health disparities and

vulnerabilities.

This analysis contributes to understanding how demographic factors may influence

the spread and impact of the pandemic.

Examining Life Expectancy and

COVID-19 Cases Across

Development Status

Explanation:

Data Visualization: Displays a scatter plot of life expectancy against

confirmed COVID-19 cases.

Color Representation: Categorizes data points by development status,

enhancing visual interpretation.

Story:

In this scatter plot analysis, life expectancy is juxtaposed against

confirmed COVID-19 cases, with data points color-coded according to

development status.

The visualization facilitates the identification of potential correlations

between socioeconomic factors and pandemic outcomes.

By examining disparities across development statuses, policymakers

can tailor interventions to address health inequities and mitigate the

impact of the pandemic on vulnerable populations.

Exploring GDP and COVID-19

Cases Across Development

Status

Explanation:

Visualization: Presents a scatter plot of GDP against confirmed COVID-

19 cases.

Color Coding: Categorizes data points by development status for

enhanced insight.

Story:

This scatter plot delves into the relationship between GDP and

confirmed COVID-19 cases, with data points color-coded based on

development status.

By examining how economic factors intersect with pandemic

outcomes, stakeholders can better understand the socioeconomic

dimensions of the crisis.

This analysis aids in identifying disparities in vulnerability and

resilience across different economic strata, guiding targeted

interventions for pandemic response and recovery.

Comparing Average GDP

Across WHO Regions

Explanation:

Visualization: Presents a bar plot of average GDP by WHO region.

Data Aggregation: Computes the mean GDP for each WHO region for

comparison.

Story:

This bar plot showcases the average GDP across different WHO

regions, providing insights into regional economic disparities.

By aggregating GDP data, the plot highlights variations in economic

development among WHO regions.

Stakeholders can use this analysis to prioritize resource allocation

and development initiatives, aiming to address economic

inequalities and promote sustainable growth globally.

Comparing Average Life

Expectancy Across WHO

Regions

Explanation:

Visualization: Displays a bar plot of average life expectancy by WHO region.

Statistical Summary: Calculates the mean life expectancy for each WHO region for

comparison.

Story:

This bar plot illustrates the average life expectancy across various WHO regions,

shedding light on regional health disparities.

By analyzing average life expectancy data, stakeholders can identify regions with

lower life expectancies and prioritize health interventions accordingly.

This analysis aids in understanding global health outcomes and guiding efforts to

improve population health and well-being across different regions.

Microsoft Power BI

Work Management

Work Completed:

Data Acquisition and Cleaning:

Description: Obtained COVID-19, global population, and economic datasets. Cleaned data to ensure

consistency and accuracy.

Responsibility: All team members.

Contributions: 100% by each member.

Initial Visualization Using D3.js:

Description: Created a pie chart of COVID-19 cases by WHO region using D3.js.

Responsibility: Siddhartha Alwala, Shiva Sai Kumar Tummala.

Contributions: 50% each.

Stacked Bar Chart of COVID-19 Cases:

Description: Developed a stacked bar chart showing COVID-19 cases by country using D3.js.

Responsibility: Vishnu Priya Palagiri, Bhargavi Kondaveeti.

Contributions: 50% each.

Cleaning Datasets Using Python and Visualization with Matplotlib:

Description: Cleaned datasets using Python. Visualized global health data with Matplotlib.

Responsibility: All team members.

Contributions: 100% by each member.

Exploring Global Population Dynamics:

Description: Analyzed population distributions and trends.

Responsibility: Siddhartha Alwala, Shiva Sai Kumar Tummala.

Contributions: 50% each.

Exploring Global Economic Patterns:

Description: Examined GDP distributions and correlations with COVID-19 cases.

Responsibility: Vishnu Priya Palagiri, Bhargavi Kondaveeti.

Contributions: 50% each.

Understanding Numeric Relationships:

Description: Investigated correlations between variables using a heatmap.

Responsibility: All team members.

Contributions: 100% by each member.

Analyzing COVID-19 Trends:

Description: Generated summary statistics and time series plots for COVID-19

data.

Responsibility: All team members.

Contributions: 100% by each member.

Visualizing COVID-19 Metrics on a World Map:

Description: Plotted COVID-19 metrics on a world map.

Responsibility: Siddhartha Alwala, Shiva Sai Kumar Tummala.

Contributions: 50% each.

Exploring Relationships Between Life Expectancy and COVID-19:

Description: Investigated the relationship between life expectancy and COVID-19

cases.

Responsibility: Vishnu Priya Palagiri, Bhargavi Kondaveeti.

Contributions: 50% each.

Overall Contributions:

Siddhartha Alwala -25% , Shiva Sai Kumar Tummala -25% , Vishnu Priya Palagiri -25% ,

Bhargavi Kondaveeti -25%

References:

1. World Health Organization. (2022). COVID-19 Dashboard. [Online]. Available:

https://www.who.int/emergencies/disease-outbreak-news/item/2020-

DON233. [Accessed: April 16, 2024].

2. D3.js Documentation: https://d3js.org/

3. Matplotlib Documentation: https://matplotlib.org/stable/contents.html

4. Pandas Documentation: https://pandas.pydata.org/docs/

Links:

https://vizhub.com/bhargavikondaveeti/c273a818d4d04170b1a620d29f5a6bbe

https://vizhub.com/bhargavikondaveeti/c273a818d4d04170b1a620d29f5a6bbe?mo

de=embed

https://gist.githubusercontent.com/bhargavikondaveeti/209440046523e6eec9b6d

4efc334f60f/raw/2d1ed33decd151c0aa10584d6e570f8175c5f0b0/project_covid_data.c

https://gist.githubusercontent.com/bhargavikondaveeti/44c211fa68660eaa099633

f5a716129b/raw/030d27261f13125b7c81aed94f2ae6c0f6ab3dd2/life_expectancy_verm

a.csv

Project Final Video.mp4

https://siddharthaalwala.github.io/CSCE_5320_Final_project/